Palantir invented the job, AI labs made it famous, and Indian IT is now trying to build thousands of them. What the work really is, what it pays, and how to show you can do it.
I first published this on Medium. Cross-posting here because a lot of the people asking me about this role are on DEV.
By Pankit Kumar, Senior Data Scientist and Lead Instructor at NeuraPath Academy
In September I watched a room of beginners write a Python program that worked perfectly and would have failed on every machine except the one it was written on.
It was a live class. We were logging events from a small simulation. The notebook wrote its own log file by hand. In five lines it managed three bugs, and it saved the file to a path that began with C:\Users\ followed by one person's username. It ran. On that laptop, on that day, it was correct.
We spent the next two classes undoing it: turning a notebook into a package, replacing the hand-rolled log file with Python's logging module, adding tests that could run on anyone's machine. Nobody enjoyed it as much as building the simulation. But that gap, between code that works for you and code that works for someone else, is the whole job of a forward deployed engineer. They spend their working life on the other side of that gap, inside a client's systems, where your laptop's assumptions are all wrong.
The title is everywhere in India right now. I want to explain what it actually means, who is really being hired, and what you can do about it if you want in. I'll use numbers where I have them and say so where I don't.
Where the job came from
The forward deployed engineer started at Palantir. Internally they were called "Deltas", and for years Palantir employed more of them than product engineers. A 2019 Palantir post put the difference in one line that is still the best definition I know: product engineers build "one capability, many customers"; Deltas deliver "one customer, many capabilities" (Palantir, Dev versus Delta).
That one-customer focus is the point. An FDE sits with a single client, learns how their data, systems and politics actually work, and then writes software that runs in that environment. Not a slide deck. Running code, on the client's infrastructure, used by the client's people.
The same Palantir post is careful to separate this from consulting. Consultants, one of their engineers said, "generally create a one-time analysis, recommendation, or solution to a specific problem." An FDE ships software that stays.
Why AI made it the job everyone is talking about
For a decade the role was a Palantir oddity. Then large language models arrived, and every software company discovered the same problem at once: a demo that impresses in a meeting falls apart on a real customer's data.
The model is fine. What breaks is everything around it. The customer's documents are scanned PDFs with handwriting. Their single sign-on rejects your service. Their compliance team wants to know where every prompt goes. Salesforce's FDE leadership described the alternative bluntly: without people who can do this work on site, "we risk having thousands of customers stuck in pilot purgatory" (Salesforce).
Investors noticed. Andreessen Horowitz's widely shared 2025 essay argued that AI startups should accept lower margins and embed engineers with customers, because enterprises buying AI "are like your grandma getting an iPhone": they want to use it, but they need someone to set it up (a16z, Trading Margin for Moat). OpenAI, Anthropic, Salesforce and a long list of startups now hire for the role, sometimes under different names: Applied AI Engineer, Deployment Strategist, Solutions Engineer.
If you want one image of the job, the best I have read comes from an engineer who did it at an AI startup. He described it as "like being a firefighter crossed with a plumber and a translator" (Het Trivedi). Firefighter, because something is always on fire. Plumber, because most of the work is connecting pipes nobody documented. Translator, because the client speaks in business problems and your tools speak in APIs.
India has two FDE job markets, not one
Most articles about this role are written from San Francisco, and they quietly assume one kind of employer. In India there are two, and they want very different people.
The two markets compared. Experience figures from our jobs snapshot and public postings; services-firm plans from Moneycontrol, July 2026.
The first market is AI labs and product companies. These are the roles that make headlines. OpenAI's FDE postings ask for five or more years of experience and up to 50% travel. Anthropic's India openings for this kind of work, Applied AI Architect roles in Bangalore and Mumbai, ask for ten years or more in technical customer-facing roles. These companies hire people who have already shipped production systems for demanding customers, and they pay accordingly.
The second market is IT services and global capability centres. Indian services firms have started building FDE teams, mostly from their own staff. Moneycontrol reported in July 2026 that TCS plans to move 1 to 1.5% of its workforce into the role, that HCLTech is training selected freshers as FDEs, that LTM's AI1000 programme aims to build a thousand of them, and that Coforge is targeting more than a hundred (Moneycontrol). The hiring here runs through internal programmes, project allocation and certifications, not through cold applications to a San Francisco lab.
Both are real. But if you are a fresher reading about a ₹50 lakh FDE package at an AI lab, you are reading about the first market, and the door into it is not where most people think.
What Indian postings actually ask for
Our academy runs a live board of FDE and related openings in India, pulled directly from employers' careers pages. On 12 October 2026 it held 138 open roles at 84 companies. That is a fraction of the market, because new roles go up every day, but it is enough to see a pattern.
Share of 138 open FDE and related roles in India mentioning each skill. Source: NeuraPath FDE jobs board, 12 Oct 2026.
The top requirement is not a technology. Client-facing work appears in almost half the postings, more than AI agents, more than any cloud platform, more than Python. Then come the tools you would expect: agents, AWS, Python, APIs, Azure, LLMs, retrieval.
The other number matters even more to anyone starting out.
Minimum experience stated in the 43 postings that state one. Most postings state none.
Of the postings that state a minimum, only two ask for a year or less. The median is five years. I am not showing this to discourage anyone. I am showing it because the honest answer to "can I become an FDE straight out of college?" depends on which market you mean, and pretending otherwise wastes people's time.
The salary question, without the hype
You will see two kinds of numbers in Indian coverage of this role.
The first kind is sensible. TeamLease Digital's figures, reported by Moneycontrol in July 2026, put entry-level FDE pay at roughly ₹18 to 25 lakh, mid-level at ₹30 to 45 lakh and senior at ₹50 to 85 lakh. These are bands from one staffing firm's data, and they describe people who already hold the title, but they are a reasonable starting point.
The second kind is the headline. "Seven times a fresher's salary" compares FDE entry pay with a typical ₹3 to 4 lakh starting package at a large services firm. That is not a like-for-like comparison. The people being hired as FDEs at ₹18 lakh are mostly not freshers; they are engineers with a few years of production work behind them. The multiple is real arithmetic, but it compares two different groups of people.
What I tell people who ask me about pay: look at who is being hired at that number before you look at the number. Live postings that print a range are the most honest data you will find. On our board that week, published ranges ran from around ₹14 to 30 lakh at an early-stage startup to ₹70 to 90 lakh at an AI company hiring for India. Wide, because the job is wide.
You may already be doing part of this job
Here is the part I wish more engineers in Indian IT knew. A lot of the FDE skill set is built in exactly the work that people in services companies are often a little embarrassed about.
If you have spent time on a client site, you have done the most important part. If you have kept a production system alive during a bad week, you have done the second most important part. Look at this list honestly:
- Have you sat with a client and worked out what they actually needed, as opposed to what the ticket said?
- Have you connected a system to data you did not design and nobody fully documented?
- Have you owned something in production, been the person who got the call, and fixed it?
- Have you explained a technical trade-off to someone who controls a budget?
- Have you changed a delivered system because the client's process turned out to be different from the spec?
If you answered yes to three of those, you are closer than your job title suggests. One FDE who moved from a large tech company into the role wrote that he googled "what is an FDE" after the recruiter's call and realised he had been doing the job unofficially for years. What is usually missing is not experience. It is the modern AI layer (agents, retrieval, evaluation) and the ability to show the work to someone outside your company.
Freshers: what is real and what isn't
I teach freshers, so I owe them a straight answer.
What isn't real: walking out of college and into an FDE role at an AI lab. Those roles ask for years of production experience, and no course or certificate replaces that.
What is real:
- Entry-level FDE roles at startups and some services firms. They exist. On our board, a handful of postings asked for one or two years, and HCLTech's fresher track shows services firms are willing to train for the role.
- Adjacent first jobs that lead there. Implementation engineer, solutions engineer, technical support engineer for an AI product, applied AI engineer. Each puts you in front of customers with a technical product, which is exactly the experience the FDE postings ask for.
- Proof that you can do the work. This is the part you control.
The fresher who gets shortlisted is not the one with the longest list of courses. It is the one who can show a system they built for a real user, working on someone else's machine, with a clear account of what went wrong and how they fixed it.
How to build proof that an FDE hiring manager will believe
Certificates are cheap and everyone has one. Here are three things that are harder to fake and that I would rather see on a CV.
An integration on messy data. Pick a public dataset that is genuinely awkward: inconsistent formats, missing values, a real API with rate limits. Build a small service that pulls it, cleans it and serves it through your own API, deployed somewhere with a link. Then write a short README that a stranger can follow without messaging you. Our own fresher curriculum ends its second phase with exactly this kind of project, because it is the closest thing to a first week on a client site.
An evaluation report for something you built with an LLM. Not a demo video. A table: these are the 50 questions I tested, this is how often the answer was right, these are the five failures and what I changed. Very few candidates do this. Every FDE has to, because a client will eventually ask "how do you know it works?"
A written-up incident. Something broke in a project of yours. Write it up properly: what happened, how you found it, what you fixed, what you would do differently. This shows judgement, which is what clients pay for and what interviews probe.
Then write about the work in public. The startup engineer I quoted earlier got his FDE role after a co-founder read a blog post he had written about deploying a model. That is not a fluke. Showing your work is how most people in this field get noticed.
What the work feels like, so you can decide
Before you chase the title, be honest with yourself about the job.
You will context-switch constantly. You will often be the person who says "this will take longer than you hoped" to someone senior who does not want to hear it. Your success is measured partly by whether a customer adopts the system, which is not entirely in your control. Some weeks involve travel. Some weeks you write very little code and spend your time in meetings working out what the problem actually is.
If that sounds draining, a product engineering role may suit you better, and there is nothing second-best about that. If it sounds like the kind of problem you enjoy, this is one of the most interesting jobs in software right now, and the Indian market for it is only beginning.
Where to start this week
- Read three real FDE postings from Indian employers and note which skills from the chart above each one names.
- Pick the one proof project from the list above that matches the job you want, and scope it to something you can finish in a month.
- If you work in services, write down your answers to the five questions above. That page is the start of your FDE story.
We keep a free, updated list of FDE openings in India, along with the numbers behind the charts in this piece, at neurapath.in/forward-deployed-engineer. If you want to talk through your own route into the role, that page is also the easiest way to reach us.
A question for the comments: if you hold an FDE, solutions or implementation title in India, which of the skills in that chart did you actually use in your first month? I suspect the answer is "client-facing" for almost everyone, and I would like to know if I'm wrong.
Pankit Kumar is a Senior Data Scientist at Parexel with ten years in data science and AI, much of it building production systems in regulated pharma and clinical settings. He leads the forward deployed engineering programmes at NeuraPath Academy in Noida.
Top comments (0)